微条目：鼓励对交互式数据系统的心智模型进行更深入的评估
Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems
摘要：
许多交互式数据系统将数据的可视化表示与用于自动化和数据探索的嵌入式算法支持相结合。为了有效地支持透明和可解释的数据系统，研究人员和设计人员必须了解用户如何理解系统。讨论了系统逻辑用户心智模型的评价问题。心智模型很难捕捉和分析。虽然常见的评估方法的目的是近似用户的最终心理模型后，一段时间的系统使用，用户的理解不断演变，用户与系统随着时间的推移进行交互。在本文中，我们回顾了许多常见的心理模型测量技术，讨论了权衡，并建议方法更深入，更有意义的评估心理模型时，使用交互式数据分析和可视化系统。我们提出的准则，随着时间的推移，揭示了特定的模型更新的演变，以及它们如何映射到特定的接口功能和数据查询的使用评估心理模型。通过要求用户描述他们知道什么以及他们是如何知道的，研究人员可以收集结构化的，时间顺序的洞察用户的概念化过程，同时也有助于引导用户自己的发现。
英文摘要：
Many interactive data systems combine visual representations of data with embedded algorithmic support for automation and data exploration. To effectively support transparent and explainable data systems, it is important for researchers and designers to know how users understand the system. We discuss the evaluation of users' mental models of system logic. Mental models are challenging to capture and analyze. While common evaluation methods aim to approximate the user's final mental model after a period of system usage, user understanding continuously evolves as users interact with a system over time. In this paper, we review many common mental model measurement techniques, discuss tradeoffs, and recommend methods for deeper, more meaningful evaluation of mental models when using interactive data analysis and visualization systems. We present guidelines for evaluating mental models over time that reveal the evolution of specific model updates and how they may map to the particular use of interface features and data queries. By asking users to describe what they know and how they know it, researchers can collect structured, time-ordered insight into a user's conceptualization process while also helping guide users to their own discoveries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
